The potential of GANs do anonymize sensitive data (such as medical records) using the generator of a GAN trained on the raw data has been effectively demonstrated in the past, the authors of this paper take a similar approach to generate data for augmentation. The model, based on image conditional Generative Adversarial Networks, takes data from a source domain and learns to take any data item and generalize it to generate other within-class data items. As this generative process does not depend on the classes themselves, it can be applied to novel unseen classes of data.